Our client, one of the largest publicly traded hospital networks in the United States, faced operational challenges in managing their emergency room (ER) operations, including fragmented data systems, difficulties in promptly identifying patients, and inefficiencies during patient transitions from ER to inpatient care. These challenges negatively impacted decision-making capabilities, slowed operational workflows, and ultimately affected patient care quality and resource allocation.
Internally, the hospital sought to modernize its systems to better leverage data-driven insights, aiming to improve real-time patient management and operational efficiency. Recognizing these specific needs, they partnered with Emids to develop a targeted solution integrating advanced AI and cloud technologies.
Solution
In collaboration with Google Cloud and UnityAI, Emids developed a proof of concept (PoC) specifically designed to address emergency room management needs. The solution focused on enhancing patient identification accuracy and improving the operational transitions between ER and inpatient care, powered by generative AI (GenAI) to quickly and accurately process patient data.
To provide actionable, real-time insights, the solution leveraged Google’s Cloud Health Data Engine (HDE), incorporating pre-General Availability (GA) GCP features for advanced analytics and improved system responsiveness. A Fast Healthcare Interoperability Resources (FHIR) store was also integrated with Google’s BigQuery, facilitating seamless data interoperability and supporting robust analytics capabilities.
Additionally, a secure real-time data streaming pipeline was constructed using GCP tools such as PubSub and DataFlow, allowing for efficient data processing and transfer. Lastly, an HL7 MLLP adaptor was implemented to ensure effective integration of the new AI-enabled system with existing hospital technologies.
Outcomes
Following the solution’s deployment, the hospital’s emergency room operations saw substantial improvements. Healthcare providers gained immediate access to accurate, AI-driven insights, significantly enhancing the speed and accuracy of patient identification and ER management. This directly supported smoother patient transitions from the ER to inpatient care.
Additionally, increased data transparency allowed healthcare professionals and hospital administrators to make more informed, timely decisions. This led to measurable improvements in patient outcomes and operational performance.
Operationally, the hospital experienced improved efficiency, demonstrated by streamlined workflows and optimized resource allocation. Reduced ER wait times and better management of care pathways resulted in tangible improvement in both patient satisfaction and clinical outcomes.
This initiative provided a robust foundation to scale future AI-driven initiatives, ensuring the hospital remains equipped to adapt effectively to future healthcare technology advancements.